About: For nearly a century, the initial reproduction number (R0) has been used as a one number summary to compare outbreaks of infectious disease, yet there is no `standard' estimator for R0. Difficulties in estimating R0 arise both from how a disease transmits through a population as well as from differences in statistical estimation method. We describe eight methods used to estimate R0 and provide a thorough simulation study of how these estimates change in the presence of different disease parameters. As motivation, we analyze the 2009 outbreak of the H1N1 pandemic influenza in the USA and compare the results from our eight methods to a previous study. We discuss the most important aspects from our results which effect the estimation of R0, which include the population size, time period used, and the initial percent of infectious individuals. Additionally, we discuss how pre-processing incidence counts may effect estimates of R0. Finally, we provide guidelines for estimating point estimates and confidence intervals to create reliable, comparable estimates of R0.   Goto Sponge  NotDistinct  Permalink

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  • For nearly a century, the initial reproduction number (R0) has been used as a one number summary to compare outbreaks of infectious disease, yet there is no `standard' estimator for R0. Difficulties in estimating R0 arise both from how a disease transmits through a population as well as from differences in statistical estimation method. We describe eight methods used to estimate R0 and provide a thorough simulation study of how these estimates change in the presence of different disease parameters. As motivation, we analyze the 2009 outbreak of the H1N1 pandemic influenza in the USA and compare the results from our eight methods to a previous study. We discuss the most important aspects from our results which effect the estimation of R0, which include the population size, time period used, and the initial percent of infectious individuals. Additionally, we discuss how pre-processing incidence counts may effect estimates of R0. Finally, we provide guidelines for estimating point estimates and confidence intervals to create reliable, comparable estimates of R0.
Subject
  • Epidemiology
  • Pandemics
  • Acoustics
  • Estimation theory
  • Filter frequency response
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